Mapping tectonic stress at subduction zones with earthquake focal mechanisms: application to Cascadia, Japan, Nankai, Mexico, and northern Chile
Bibliographic record
Abstract
Earthquake focal mechanisms have contributed substantially to our understanding of modern tectonic stress regimes, perhaps more than any other data source. Studies generally group focal mechanisms by epicentral location to examine variations in stress across a region. However, stress variations with depth have rarely been considered, either due to data limitations or because they were believed to be negligible. This study presents 3D grids of tectonic stress tensors using existing focal mechanism catalogs from several subduction zones, including Cascadia, Japan, Nankai, Mexico, and northern Chile. We bin data into 50 x 50 x 10 km cells (north, east, vertical), with 50% overlap in all three directions. This resulted in 181380 stress inversions, with 90% of these in Japan (including Nankai). To the best of our knowledge, this is the first examination of stress changes with depth in several of these regions. The resulting maps and cross-sections of stress can help distinguish locked and creeping segments of the plate interface. Similarly, by dividing the focal mechanism catalog in northern Japan into those before and those >6 months after the 2011 Mw 9.1 Tohoku-Oki earthquake, we are able to produce detailed 3D maps of stress rotation, which is close to 90° near the areas of highest slip. These results could inform geodynamic rupture models of future megathrust earthquakes in order to more accurately estimate slip, shaking, and seismic hazard. Southern Cascadia and Nankai appear to have sharp stress discontinuities at ~20 km depth, and northern Cascadia may have a similar discontinuity at ~30 km depth. These stress boundaries may relate to rheological discontinuities in the forearc, and may help us unravel how forearc composition influences subduction zone behaviour and seismic hazard.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".